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内容简介
Chapter 1 Introduction
1.1 Background and Motivation
1.2 Contributions
1.2.1 Extended MDLP for Transfer Learning
1.2.2 Compact Coding for Hyperplane Classifiers in Transfer Learning
1.2.3 Transfer Active Learning
1.2.4 Gaussian Process for Transfer Learning
1.3 Book Overview
Chapter 2 Literature Review and Preliminaries for MDLP
2.1 Transfer Learning
2.2 Active Learning and Transfer Active Learning
2.3 Preliminaries for MDLP
Chapter 3 Extended MDL Principle for Feature-based Transfer Learning
3.1 Introduction
3.2 Problem Statement
3.3 Preliminaries for Encoding
3.3.1 Theoretical Foundation of the EMDLP
3.3.2 Adaptation of the EMDLP to Our Problem
3.4 Supervised Inductive Transfer Learning Algorithm
3.4.1 EMDLP with Incremental Search
3.4.2 EMDLP with Hill Climbing
3.5 Experiments
3.5.1 Experimental Settings
3.5.2 Experimental Results on Synthetic Data Sets
3.5.3 Experimental Results on Real Data Sets
3.6 Summary
Chapter 4 Compact Coding for Hyperplane Classifiers in a Heterogeneous Environment
4.1 Introduction
4.2 Problem Setting
4.3 Compact Coding for Hyperplane Classifiers in Heterogeneous Environment
4.3.1 Macro Level:Arrange Related Tasks
4.3.2 Micro Level Evaluation
4.3.3 The Transfer Learning Algorithm
4.4 Experiments
4.4.1 Experimental Setting
4.4.2 Experimental Results
4.5 Summary
Chapter 5 Adaptive Transfer Learning with Query by Committee
5.1 Introduction
5.2 Problem Setting and Preliminaries
5.3 Probabilistic Framework for ALTL
5.4 The ALTL Algorithm and Analysis
5.4.1 The Procedure of ALTL
5.4.2 Termination Condition and Analysis
5.5 Experiments
5.5.1 Experimental Setting
5.5.2 Results on Synthetic Data Sets
5.5.3 Results on Real Data Sets
5.6 Summary
Chapter 6 Gaussian Process for Transfer Learning through Minimum Encoding
6.1 Introduction
6.2 Gaussian Process for Classification
6.3 The GPTL Algorithm
6.3.1 Arrange Related Tasks
6.3.2 The Instance Level Similarities
6.4 Experiments
6.5 Summary
Chapter 7 Concluding Comments
Appendix A Target Concepts in Chapter 3
Bibliography
